Multidimensional orthogonal FM transforms.

The present a novel class of multidimensional orthogonal FM transforms. The analysis suggests a novel signal-adaptive FM transform possessing interesting energy compaction properties. We show that the proposed signal-adaptive FM transform produces point spectra for multidimensional signals with uniformly distributed samples. This suggests that the proposed transform is suitable for energy compaction and subsequent coding of broadband signals and images that locally exhibit significant level diversity. We illustrate these concepts with simulation experiments.

Further results on MAP optimality and strong consistency of certain classes of morphological filters.

Morphological openings and closings can be viewed as consistent MAP estimators of smooth random binary image signals immersed in i.i.d. clutter, or suffering from i.i.d. random dropouts. We revisit this viewpoint under much more general assumptions and show that, quite surprisingly, the above interpretation is still valid.

MAP signal estimation in noisy sequences of morphologically smooth images.

Sidiropoulos et al. (1994) demonstrated that morphological openings and closings can be viewed as maximum a posteriori (MAP) estimators of morphologically smooth signals in signal-independent i.i.d. noise. The present authors extend these results to the M-fold independent observation case, and show that the aforementioned estimators are strongly consistent. We also demonstrate the validity of a thresholding conjecture (Sidiropoulos et al., 1994) by simulation, and use it to evaluate estimator performance. Taken together, these results can help determine the least upper bound, M , on M, which guarantees virtually error-free reconstruction of morphologically smooth images.

Optimal filtering of digital binary images corrupted by union/intersection noise.

We model digital binary image data as realizations of a uniformly bounded discrete random set (or discrete random set, for short), which is a mathematical object that can be directly defined on a finite lattice. We consider the problem of estimating realizations of discrete random sets distorted by a degradation process that can be described by a union/intersection noise model. Two distinct optimal filtering approaches are pursued. The first involves a class of "mask" filters, which arises quite naturally from the set-theoretic analysis of optimal filters. The second approach involves a class of morphological filters. We prove that under i.i.d noise morphological openings, closings, unions of openings, and intersections of closings can be viewed as MAP estimators of morphologically smooth signals. Then, we show that by using an appropriate (under a given degradation model) expansion of the optimal filter, we can obtain universal characterizations of optimality that do not rely on strong assumptions regarding the spatial interaction of geometrical primitives of the signal and the noise. The results generalize to gray-level images in a fairly straightforward manner.